Hostname: page-component-745bb68f8f-mzp66 Total loading time: 0 Render date: 2025-02-02T22:19:30.130Z Has data issue: false hasContentIssue false
  • English
  • Français

Reactive and the shortest path navigation of a wheeled mobile robot in cluttered environments

Published online by Cambridge University Press:  10 July 2012

Andrey V. Savkin  and
Michael Hoy
Andrey V. Savkin
Affiliation:
School of Electrical Engineering and Telecommunication, University of New South Wales, Sydney, Australia
Michael Hoy*
Affiliation:
School of Electrical Engineering and Telecommunication, University of New South Wales, Sydney, Australia
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

We determine the shortest (minimal in length) path on a unicycle-like mobile robot in a known environment with smooth (possibly non-convex) obstacles with a constraint on curvature of their boundaries. Furthermore, we propose a new reactive randomized algorithm of robot navigation in unknown environment and prove that the robot will avoid collisions and reach a steady target with probability 1. The performance of our algorithm is confirmed by computer simulations and outdoor experiments with a Pioneer P3-DX mobile wheeled robot.

Keywords

Obstacle avoidanceRobot navigationCluttered environmentsWheeled mobile robotsMinimum length pathShortest path planningReactive navigationGlobal path planning
Type
Articles
Information
Robotica , Volume 31 , Issue 2 , March 2013 , pp. 323 - 330
Copyright
Copyright © Cambridge University Press 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Belkhous, S., Azzouz, A., Saad, M., Nerguizian, V. and Nerguizian, C., “A novel approach for mobile robot navigation with dynamic obstacle avoidance,” J. Intell. Robot. Syst. 44 (3), 187–201 (2005).CrossRefGoogle Scholar
Deng, M., Inoue, A., Shibata, Y., Sekiguchi, K. and Ueki, N., “An Obstacle Avoidance Method for Two Wheeled Mobile Robot,” In: Proceedings of the IEEE International Conference on Networking, Sensing and Control, London, UK (Apr. 15–17, 2007) pp. 689–692.Google Scholar
Dubins, L. E., “On curves of minimal length with a constraint on average curvature and with prescribed initial and terminal positions and tangents,” Am. J. Math. 79, 497–516 (1957).CrossRefGoogle Scholar
Kamon, I., Rimon, E. and Rivlin, E., “Tangentbug: A range-sensor-based navigation algorithm,” Int. J. Robot. Res. 17 (9), 934–953 (1998).CrossRefGoogle Scholar
Kamon, I. and Rivlin, E., “Sensory-based motion planning with global proofs,” IEEE Trans. Robot. Autom. 13 (6), 817–822 (1997).CrossRefGoogle Scholar
Kolmogorov, A. N. and Fomin, S. V., Elements of the Theory of Functions and Functional Analysis (Courier Dover, N. Chemsford, MA, 1999).Google Scholar
Lapierre, L., Zapata, R. and Lepinay, P., “Combined path following and obstacle avoidance control of a wheeled robot,” Int. J. Robot. Res. 26 (4), 361–375 (2007).CrossRefGoogle Scholar
Liu, Y. H. and Arimoto, S., “Path planning using a tangent graph for mobile robots among polygonal and curved obtacles,” Int. J. Robot. Res. 11 (4), 376–382 (1992).CrossRefGoogle Scholar
Manchester, I. R. and Savkin, A. V., “Circular navigation missile guidance with incomplete information and uncertain autopilot model,” J. Guid. Control Dyn. 27 (6), 1078–1083 (2004).CrossRefGoogle Scholar
Manchester, I. R. and Savkin, A. V., “Circular navigation guidance law for precision missile target engagement,” J. Guid. Control Dyn. 29 (2), 314–320 (2006).CrossRefGoogle Scholar
Matveev, A. S., Teimoori, H. and Savkin, A. V., “A method for guidance and control of an autonomous vehicle in problems of border patrolling and obstacle avoidance,” Automatica 47 (3), 515–524 (2011).CrossRefGoogle Scholar
Matveev, A. S., Teimoori, H. and Savkin, A. V., “Navigation of a unicycle-like mobile robot for environmental extremum seeking,” Automatica 47 (1), 85–91 (2011).CrossRefGoogle Scholar
Matveev, A. S., Teimoori, H. and Savkin, A. V., “Range-only measurements based target following for wheeled mobile robots,” Automatica 47 (1), 177–184 (2011).CrossRefGoogle Scholar
Savkin, A. V. and Teimoori, H., “Bearings-only guidance of a unicycle-like vehicle following a moving target with a smaller minimum turning radius,” IEEE Trans. Autom. Control 55 (10), 2390–2395 (2010).CrossRefGoogle Scholar
Shiller, Z., “Online suboptimal obstacle avoidance,” Int. J. Robot. Res. 19 (5), 480–497 (2000).CrossRefGoogle Scholar
Struik, D. J., Lectures on Classical Differential Geometry (Courier Dover, N. Chemsford, MA, 1988).Google Scholar
Teimoori, H. and Savkin, A. V., “A biologically inspired method for robot navigation in a cluttered environment,” Robotica 28 (5), 637–648 (2010).CrossRefGoogle Scholar
Teimoori, H. and Savkin, A. V., “Equiangular navigation and guidance of a wheeled mobile robot based on range-only measurements,” Robot. Auton. Syst. 58 (2), 203–215 (2010).CrossRefGoogle Scholar
Vlassis, N., Sgouros, N., Efthivolidis, G. and Papakonstantinou, G., “Global Path Planning for Autonomous Navigation,” In: Proceedings of the 8th IEEE International Conference on Tools with AI, Toulouse, France (Nov. 16–19, 1996) pp. 354–359.Google Scholar